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Record W2186852840 · doi:10.5858/arpa.2013-0173-cp

Performance Characteristics of Adenoid Cystic Carcinoma of the Salivary Glands in Fine-Needle Aspirates: Results From the College of American Pathologists Nongynecologic Cytology Program

2015· article· en· W2186852840 on OpenAlexaff
Z. Laura Tabatabai, Manon Auger, Daniel F. I. Kurtycz, Alice Laser, Rhona J. Souers, Rodolfo Laucirica, Güliz A. Barkan, Barbara A. Crothers, Walid E. Khalbuss

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsConcordanceMedicineAdenoid cystic carcinomaCytologyPapanicolaou stainPathologySalivary glandAcinic cell carcinomaMalignancyCytopathologyCarcinomaRadiologyMucoepidermoid carcinomaInternal medicineCancer

Abstract

fetched live from OpenAlex

CONTEXT: Although the cytomorphology of adenoid cystic carcinoma (ACC) has been well described, the accuracy of this diagnosis in fine-needle aspirates (FNAs) of the salivary glands has not been extensively evaluated. OBJECTIVE: To assess participants' responses in the College of American Pathologists (CAP) Nongynecologic Cytology (NGC) Program to determine the accuracy and false-negative rate of ACC cases in salivary gland FNAs. DESIGN: A retrospective review of the CAP NGC Program's cumulative data from 2000-2010 was performed for the general and the specific reference diagnosis categories for ACC in salivary gland FNAs according to preparation and participant types. RESULTS: Of 5156 responses, the overall concordance rates for both the general category of malignancy and the specific category of ACC were 63.6% (3279 of 5156) and 38.6% (1966 of 5088), respectively, with a false-negative rate of 36.4% (1877 of 5156). The most frequent false-negative responses were pleomorphic (1080) and monomorphic (526) adenoma (1614 of 5088, 31.5%), while lymphoma was the most frequent malignant misinterpretation. There was a significant statistical difference in concordance to the reference interpretation between the reader types: 39.9% (1006 of 2521) concordance rate for pathologists compared to 33.8% (503 of 1488) for cytotechnologists. However, there was no significant statistical difference for concordance to the general category or reference interpretation, based on preparation type (Papanicolaou versus modified Giemsa stained). CONCLUSIONS: In this interlaboratory comparison educational program, accurate identification of ACC has shown to be problematic, with ACC representing an important cause of false-negative responses. The most common diagnostic pitfall is distinguishing this entity from pleomorphic and monomorphic adenoma in the benign category and from lymphoma and adenocarcinoma in the malignant one.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.275
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations32
Published2015
Admission routes1
Has abstractyes

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